Senior ML Engineer Jobs in Palo Alto, CA
Senior ML Engineer jobs in Palo Alto are concentrated across AI research, enterprise software, and semiconductor design, with hiring anchored in downtown Palo Alto, Stanford Research Park, and the East Meadow corridor. Employers hiring right now include JPMorganChase, Rivian, and Tesla. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 758+ Senior ML Engineer jobs
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company.
As an Applied Machine Learning Scientist within our dynamic team, you will lead the development of scalable, production-grade advanced ML solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will play a key role in delivering Generative AI capabilities – designing and productionizing LLM-powered systems such as RAG (Retrieval Augmented Generation), tool/function-calling agents, and structured generation to automate complex workflows and improve customer experiences. You will collaborate with product, engineering, and control partners to translate ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. You bring strong deep learning and transformer-based modeling expertise, as well as hands-on experience in fine-tuning and evaluation. You must have a strong passion for machine learning, strong analytical thinking, a deep desire to learn, and high motivation. You must also invest independent time in learning, researching, and experimenting with new innovations, and contribute to a strong knowledge-sharing culture.
Job Responsibilities
Lead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.
Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails.
Use reinforcement learning (policy optimization, bandits, RLHF‑style approaches where appropriate) to improve personalization, dialog policies, and sequential decision‑making systems.
Fine-tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.
Select and innovate on ML strategies for various banking problems.
Analyze and evaluate the ongoing performance of developed ML systems.
Collaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.
Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).
Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.
Required qualifications, capabilities, and skills
MS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
Good understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.
Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.
Experience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)
Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.
Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re-ranking) and/or recommendation systems.
Ability to design experiments — establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn), and ability to write production-quality code.
Ability to collaborate in cross-functional environments with product, engineering, and control partners.
Solid written and spoken communication skills
Preferred qualifications, capabilities, and skills
5 years of hands-on experience with virtual assistant model development and optimization
Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks.
Building agent governance and eval suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools
Experience with RL/bandits, preference optimization, or human feedback loops for personalization.
Experience in regulated finance domains and working with risk/control processes.
Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.
Experience with A/B experimentation and data/metric-driven product development.
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT USChase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company.
As an Applied Machine Learning Scientist within our dynamic team, you will lead the development of scalable, production-grade advanced ML solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will play a key role in delivering Generative AI capabilities – designing and productionizing LLM-powered systems such as RAG (Retrieval Augmented Generation), tool/function-calling agents, and structured generation to automate complex workflows and improve customer experiences. You will collaborate with product, engineering, and control partners to translate ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. You bring strong deep learning and transformer-based modeling expertise, as well as hands-on experience in fine-tuning and evaluation. You must have a strong passion for machine learning, strong analytical thinking, a deep desire to learn, and high motivation. You must also invest independent time in learning, researching, and experimenting with new innovations, and contribute to a strong knowledge-sharing culture.
Job Responsibilities
Lead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.
Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails.
Use reinforcement learning (policy optimization, bandits, RLHF‑style approaches where appropriate) to improve personalization, dialog policies, and sequential decision‑making systems.
Fine-tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.
Select and innovate on ML strategies for various banking problems.
Analyze and evaluate the ongoing performance of developed ML systems.
Collaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.
Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).
Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.
Required qualifications, capabilities, and skills
MS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
Good understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.
Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.
Experience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)
Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.
Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re-ranking) and/or recommendation systems.
Ability to design experiments — establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn), and ability to write production-quality code.
Ability to collaborate in cross-functional environments with product, engineering, and control partners.
Solid written and spoken communication skills
Preferred qualifications, capabilities, and skills
5 years of hands-on experience with virtual assistant model development and optimization
Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks.
Building agent governance and eval suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools
Experience with RL/bandits, preference optimization, or human feedback loops for personalization.
Experience in regulated finance domains and working with risk/control processes.
Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.
Experience with A/B experimentation and data/metric-driven product development.
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
ABOUT USChase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
See All 758+ Senior ML Engineer Jobs in Palo Alto
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Find Senior ML Engineer JobsSenior ML Engineer Job Market in Palo Alto
Who's Hiring
- JPMorganChase139
- Rivian108
- Tesla77
- Snap62
- GEICO46
Top Industries Hiring
- Technology & Software124
- Biotechnology & Pharmaceuticals46
- Insurance31
- Social Media31
- Manufacturing31
Senior ML Engineer Jobs in Palo Alto: Frequently Asked Questions
How do I get a senior ml engineer job in Palo Alto?
The clearest path into Palo Alto's senior ml engineer market is targeting the research-heavy organizations clustered around Stanford Research Park and the venture-backed AI startups along University and Embarcadero corridors. Candidates who demonstrate applied ML experience in computer vision, large language models, or recommendation systems stand out most. Publishing on arXiv, contributing to open-source ML frameworks, and attending local AI meetups all build the visibility that Palo Alto hiring teams respond to.
Which companies hire senior ml engineers in Palo Alto?
Employers hiring senior ml engineers in Palo Alto right now include JPMorganChase, Rivian, and Tesla, based on current listings on Migrate Mate as of November 2026. Palo Alto's employer mix leans heavily toward AI-native startups, established semiconductor and hardware firms, and the research arms of large technology companies with Peninsula campuses.
Are there remote senior ml engineer jobs in Palo Alto?
Yes, though it varies by role type. About 43% of senior ml engineer openings tied to Palo Alto are remote or hybrid as of November 2026, with fully remote options more common for roles focused on model training, MLOps, and data pipeline work than for positions requiring on-site lab access or hardware integration.
How can I get a senior ml engineer job in Palo Alto with little or no experience?
The most realistic entry path in Palo Alto is landing a junior ML or data scientist role at one of the area's many AI startups or applied research labs, which tend to promote quickly when you show ownership over model delivery. Stanford-affiliated programs and incubators around University Avenue regularly bring on early-career engineers for contract or project-based work, giving candidates a portfolio of real Palo Alto deployments that opens doors to senior-level conversations faster than a national job search would.
Which industries hire the most senior ml engineers in Palo Alto?
Most senior ml engineer openings in Palo Alto sit in Technology & Software, Biotechnology & Pharmaceuticals, and Insurance, per current listings on Migrate Mate as of November 2026. Palo Alto's proximity to Stanford and its dense concentration of deep-tech venture capital means those sectors consistently generate the highest demand for engineers who can move models from research into production.
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